1. Bioinformatics in transplantation immunology PhD defense Malene Erup Larsen October 27th 2010

2. 27/10/2010Bioinformatics in transplantation immunology2CBS, Department of Systems Biology Hematopoietic cell transplantations Matching donor Leukemia patient Hematopoietic stem cells + leukocytes Ideal outcome: Hematopoietic system is replaced by a healthy one Leukemia relapse is prevented due to the graft- versus-leukemia effect Complications: Graft-versus-host disease Relapse Graft rejection Main topic of PhD: Prediction of minor histocompatibility antigens (mHags)

3. 27/10/2010Bioinformatics in transplantation immunology3CBS, Department of Systems Biology Outline of talk Introduction Prediction of H-Y antigens Prediction of nsSNP-derived mHags Survival study HLArestrictor Summary

4. 27/10/2010Bioinformatics in transplantation immunology4CBS, Department of Systems Biology Antigen presentation MHC = Major Histocompatibility Complex HLA = Human Leukocyte Antigen TAP = Transporter associated with Antigen Processing ER = Endoplasmic reticulum Figure by Mette Voldby Larsen

5. 27/10/2010Bioinformatics in transplantation immunology5CBS, Department of Systems Biology Peptide / MHC binding HLA-A*0201 LLFGYPVYV VLHDDLLEA YIGEVLVSV RTLDKVLEV FIDSYICQV SLYNTVATL......... Acidic Basic Hydrophobic Neutral PDB structure 1DUZ From: MHC motif viewer

6. 27/10/2010Bioinformatics in transplantation immunology6CBS, Department of Systems Biology NetMHCpan Artificial neural network trained on available binding data and residues in contact with the peptide M. Nielsen, et al., PLoS ONE 2, e796 (2007) Standard binding threshold: Affinity = IC50 = 500 nM Concentration of peptides at which half of the HLA molecules are occupied % rank value: at 2% rank, only 2% of random peptides are predicted to have a stronger binding than the query peptide

7. 27/10/2010Bioinformatics in transplantation immunology7CBS, Department of Systems Biology T cell education Hematopoietic stem cells develop into T cell precursors T cells are educated in the thymus Naive T cells await to be activated

8. 27/10/2010Bioinformatics in transplantation immunology8CBS, Department of Systems Biology Thymal training Illustration from Elsevier images

9. 27/10/2010Bioinformatics in transplantation immunology9CBS, Department of Systems Biology Allogeneic hematopoietic cell transplantation - minor histocompatibility antigens MHCI T-cells Patient cells T-cell Stem cells T cell education in the thymus After allo-HCT PatientDonor mHag Figure by Mette Voldby Larsen

10. 27/10/2010Bioinformatics in transplantation immunology10CBS, Department of Systems Biology Known mHags dbMinor lists ~30 mHags ~50 mHags are known 3 million genetic differences between any two individuals Tip of the iceberg....

11. 27/10/2010Bioinformatics in transplantation immunology11CBS, Department of Systems Biology Why identify more mHags? To better predict occurrence of GVHD Therapeutic mHags - adoptive immunotherapy

12. 27/10/2010Bioinformatics in transplantation immunology12CBS, Department of Systems Biology Outline of talk Introduction Prediction of H-Y antigens Prediction of nsSNP-derived mHags Survival study HLArestrictor Summary

13. 27/10/2010Bioinformatics in transplantation immunology13CBS, Department of Systems Biology Sex-mismatched transplantations Female donor Male patient Proteins encoded by the Y chromosome are unknown to the female immune system

14. 27/10/2010Bioinformatics in transplantation immunology14CBS, Department of Systems Biology Correlation with outcome From Stern et al. 2008 Based on data from 54,000 patients

15. 27/10/2010Bioinformatics in transplantation immunology15CBS, Department of Systems Biology Aim of the study - Apply reverse immunology to identify novel H-Y antigens Standard immunologyReverse immunology Predict epitopes High throughput peptide testing Isolated T cell clone recognizing unknown epitope Experiments SPSVDKARAEL

16. 27/10/2010Bioinformatics in transplantation immunology16CBS, Department of Systems Biology Patients 32 male patients 26 sisters 1 mother 5 unrelated donors 15 most common HLA alleles (from a total of 31 different alleles) AlleleNo. patientsAlleleNo. patients HLA-A*02:0116HLA-B*44:028 HLA-A*03:017HLA-B*08:018 HLA-A*01:016HLA-B*40:017 HLA-A*11:016HLA-B*07:026 HLA-A*24:026HLA-B*51:015 HLA-A*68:016HLA-B*35:014 HLA-A*32:012HLA-B*15:014 HLA-B*13:023

17. 27/10/2010Bioinformatics in transplantation immunology17CBS, Department of Systems Biology Predictions >SMCY (length 1570 aa) MEPGCDEFLPPPECPVFEPSWAEFQDPLGYIAKIRPIAEKSGICKIRPP ADWQPPFAVEVDNFRFTPRVQRLNELEAQTRVKLNYLDQIAKFWEIQ GSSLKIPNVERKILDLYSLSKIVI.... NetMHCpan was used to predict 8, 9, 10, and 11mers Gives MEPGCDEFLPP MEPGCDEFLP MEPGCDEFL MEPGCDEF EPGCDEFLPPP EPGCDEFLPP EPGCDEFLP EPGCDEFL Predictions were run for all 31 HLA alleles using a standard binding threshold of 500 nM resulting in 7390 predicted binders

18. 27/10/2010Bioinformatics in transplantation immunology18CBS, Department of Systems Biology Filtering steps Exclude peptides also found in homologous proteins encoded by the X chromosome Exclude shorter peptide version of a predicted binder Only include peptides predicted to bind to the 15 most common HLA alleles Include only the 30 strongest binders for each HLA allele Result: 324 predicted H-Y antigens to test experimentally

19. 27/10/2010Bioinformatics in transplantation immunology19CBS, Department of Systems Biology Experimental validations - at Laboratory of Experimental Immunology, Panum, University of Copenhagen Intracellular cytokine staining 8 patients have been tested 35 CD8+ T cell responses to 30 different peptides (1 known H-Y antigen) Binding of peptides or submers confirmed in 26 / 35 cases Next step: Tetramer validations Identify the exact sequence of the H-Y antigens Identify the restricting HLA alleles Tetramer validations

20. 27/10/2010Bioinformatics in transplantation immunology20CBS, Department of Systems Biology Outline of talk Introduction Prediction of H-Y antigens Prediction of nsSNP-derived mHags Survival study HLArestrictor Summary

21. 27/10/2010Bioinformatics in transplantation immunology21CBS, Department of Systems Biology Aim of the study - Apply reverse immunology to identify novel nsSNP derived mHags Size of the human genome compared to the Y chromosome or viral or bacterial genomes nsSNP variants are individual - genotyping is necessary mHags need to be expressed in relevant tissues Challenges

22. 27/10/2010Bioinformatics in transplantation immunology22CBS, Department of Systems Biology Selected proteins Proteins with known mHags Expression Additional proteins Expression AKAP13 SP110 BCL2A1 KIA0020 MYO1G HMHB1 USP9Y DDX3Y RPS4Y1 SMCY UTY HMHA1 CTSH ECGF1 LHR1 TOR3A Broad Hematopoietic Broad Hematopoietic B cell specific Broad Hematopoietic Broad Tumor specific Broad BCL6 CD99 TYR MAGEA1 CD3D CD79B CMRF35 IL2 TP53 WT1 TAL1 MPL NOV PLAT B cell leukemia T cell specific Melanoma T cell specific B cell specific Hematopoietic Tumor specific T cell leukemia Leukemia Broad / Cancer Endothelial cells Proteins with known mHags Expression Additional proteins Expression AKAP13 SP110 BCL2A1 KIA0020 MYO1G HMHB1 USP9Y DDX3Y RPS4Y1 SMCY UTY HMHA1 CTSH ECGF1 LHR1 TOR3A Broad Hematopoietic Broad Hematopoietic B cell specific Broad Hematopoietic Broad Tumor specific Broad BCL6 CD99 TYR MAGEA1 CD3D CD79B CMRF35 IL2 TP53 WT1 TAL1 MPL NOV PLAT B cell leukemia T cell specific Melanoma T cell specific B cell specific Hematopoietic Tumor specific T cell leukemia Leukemia Broad / Cancer Endothelial cells

23. 27/10/2010Bioinformatics in transplantation immunology23CBS, Department of Systems Biology Patients 164 patients treated with an allo-HCT between years 2000-2008 HLA identical related or fully matched unrelated donors were used 46 different HLA alleles

24. 27/10/2010Bioinformatics in transplantation immunology24CBS, Department of Systems Biology Predictions Patients were genotyped for 173 nsSNPs and variation in the GVH-direction was found in 36 nsSNPs Validation of 128 predicted mHags is ongoing Example, patient 273 Protein nsSNP Patient Donor Reference peptide Prediction, A*03:01 Missense peptide Prediction, A*03:01 AKAP13 rs7177107 KE KE EE KLCDNIVSE 10124 nM KLCDNIVSK 38 nM (Heterozygote frequency = 11 %)

25. 27/10/2010Bioinformatics in transplantation immunology25CBS, Department of Systems Biology Outline of talk Introduction Prediction of H-Y antigens Prediction of nsSNP-derived mHags Survival study HLArestrictor Summary

26. 27/10/2010Bioinformatics in transplantation immunology26CBS, Department of Systems Biology Aim of the study - Investigate possible correlations between predicted mHags and transplantation outcome M. E. Larsen et al., Biol Blood Marrow Transplant Oct. 2010, 16(10):1370-81

27. 27/10/2010Bioinformatics in transplantation immunology27CBS, Department of Systems Biology Correlation with number of nsSNP disparities? M. E. Larsen et al., Biol Blood Marrow Transplant Oct. 2010, 16(10):1370-81

28. 27/10/2010Bioinformatics in transplantation immunology28CBS, Department of Systems Biology Correlation with number of mHag disparities M. E. Larsen et al., Biol Blood Marrow Transplant Oct. 2010, 16(10):1370-81

29. 27/10/2010Bioinformatics in transplantation immunology29CBS, Department of Systems Biology Multivariate analysis Patient-donor relation Sex-mismatch Disease level (Kahl score) CMV status Patient and donor age Acute and Chronic GVHD Correlation between number of predicted mHags and survival still significant (P=0.014) when including the following covariates: M. E. Larsen et al., Biol Blood Marrow Transplant Oct. 2010, 16(10):1370-81

30. 27/10/2010Bioinformatics in transplantation immunology30CBS, Department of Systems Biology Conclusions First study to demonstrate correlation between the number of predicted mHags and transplantation outcome The effect is more significant when adding HLA binding predictions instead of only nsSNP-disparities M. E. Larsen et al., Biol Blood Marrow Transplant Oct. 2010, 16(10):1370-81

31. 27/10/2010Bioinformatics in transplantation immunology31CBS, Department of Systems Biology Outline of talk Introduction Prediction of H-Y antigens Prediction of nsSNP-derived mHags Survival study HLArestrictor Summary

32. 27/10/2010Bioinformatics in transplantation immunology32CBS, Department of Systems Biology Scientific questions What is the binding affinity between the 9mer SLYNTVATL and HLA-A*02:01? NetMHCpan: HLArestrictor: What is the optimal epitope and restricting HLA allele of the 17mer TGSEELRSLYNTVATLY known to elicit a T cell response in patient N067 with HLA alleles HLA-A*02:01, HLA-A*02:05, HLA-B*51:01, HLA-B*58:01, HLA-C*07:01, HLA-C*16:02? M. E. Larsen et al., submitted to Immunogenetics Aug. 2010

33. 27/10/2010Bioinformatics in transplantation immunology33CBS, Department of Systems Biology Interface

34. 27/10/2010Bioinformatics in transplantation immunology34CBS, Department of Systems Biology Output - HLA oriented # HLArestrictor with NetMHCpan version 2.3 # HLA types used: HLA-A02:01, HLA-A02:05, HLA-B51:01, HLA-B58:01, HLA-C07:01, HLA-C16:02 # Peptide lengths: 8, 9, 10, 11 # Sort-method: OR. Sort-mode: HLA-oriented # %rank threshold for strong binding peptides: 0.5%rank # %rank threshold for weak binding peptides: 2.0%rank # Affinity threshold for strong binding peptides: 50.0nM # Affinity threshold for weak binding peptides: 500.0nM # Number of predictions per peptide: Not specified # Non-binders shown up to a prediction score of 2.0*(weak binding threshold) Results for Peptide N067_TGSEELRSLYNTVATLY: TGSEELRSLYNTVATLY -------------------------------------------------------------------------- Pos LengthPeptideHLA1-log50k(aff) Affinity(nM) %RankLabelEstimated accuracy 8 9SLYNTVATLHLA-A02:010.43 475 4.0Combined binder0.853 8 8SLYNTVATHLA-A02:010.363 985 5.0Non-binder0.853 7 11RSLYNTVATLYHLA-B58:010.601 75 0.4Strong binder0.853 7 10RSLYNTVATLHLA-B58:010.512 196 0.8Weak binder0.853 7 10RSLYNTVATLHLA-C07:010.476 289 0.1Strong binder0.486 7 11RSLYNTVATLYHLA-C07:010.418 544 0.25Strong binder0.486 7 8RSLYNTVAHLA-C07:010.304 1867 1.5Weak binder0.486 7 9RSLYNTVATHLA-C07:010.268 2747 3.0Non-binder0.486 7 11RSLYNTVATLYHLA-C16:020.112 NA 0.8Weak binder0.564 7 10RSLYNTVATLHLA-C16:020.106 NA 0.8Weak binder0.564 M. E. Larsen et al., submitted to Immunogenetics Aug. 2010

35. 27/10/2010Bioinformatics in transplantation immunology35CBS, Department of Systems Biology Benchmark 1067 HIV ELIspot responses to 85 distinct peptides to which HLA restriction was assigned through association studies Measuring the ability of HLArestrictor to identify the correct HLA restriction element Binding threshold (%) Performance 2% rank threshold: ~90% of positives correctly predicted ~60% of negatives correctly predicted MCC ~0.4 M. E. Larsen et al., submitted to Immunogenetics Aug. 2010

36. 27/10/2010Bioinformatics in transplantation immunology36CBS, Department of Systems Biology Benchmark 18 tetramer validations Investigating the ability of HLArestrictor to identify the correct minimal epitope............ 16 / 18 minimal epitopes predicted at 2 %rank threshold 18 / 18 predicted at 2 %rank OR 500 nM threshold (standard setting) M. E. Larsen et al., submitted to Immunogenetics Aug. 2010

37. 27/10/2010Bioinformatics in transplantation immunology37CBS, Department of Systems Biology Summary of talk Overall theme: Identification of novel mHags H-Y antigens predicted and 35 CD8+ T cell responses to 30 different peptides observed in 8 patients nsSNP-derived mHags predicted in 30 selected proteins Correlation between number of predicted mHags and transplantation outcome demonstrated New flexible prediction tool - HLArestrictor - developed and benchmarked

38. 27/10/2010Bioinformatics in transplantation immunology38CBS, Department of Systems Biology Acknowledgements Søren Brunak Mette Voldby Larsen Morten Nielsen Ole Lund and the Immunological Bioinformatics group The Integrative Systems Biology group All our collaborators at Rigshospitalet and Panum Everyone at CBS Friends and family